Recent advances in managing differentiated thyroid cancer

The main clinical challenge in the management of thyroid cancer is to avoid over-treatment and over-diagnosis in patients with lower-risk disease while promptly identifying those patients with more advanced or high-risk disease requiring aggressive treatment. In recent years, novel clinical and molecular data have emerged, allowing the development of new staging systems, predictive and prognostic tools, and treatment approaches. There has been a notable shift toward more conservative management of low- and intermediate-risk patients, characterized by less extensive surgery, more selective use of radioisotopes (for both diagnostic and therapeutic purposes), and less intensive follow-up. Furthermore, the histologic classification; tumor, node, and metastasis (TNM) staging; and American Thyroid Association risk stratification systems have been refined, and this has increased the number of patients in the low- and intermediate-risk categories. There is now a need for new, prospective data to clarify how these changing practices will impact long-term outcomes of patients with thyroid cancer, and new follow-up strategies and biomarkers are still under investigation. On the other hand, patients with more advanced or high-risk disease have a broader portfolio of options in terms of treatments and therapeutic agents, including multitarget tyrosine kinase inhibitors, more selective BRAF or MEK inhibitors, combination therapies, and immunotherapy

Utilizzo di scores multiparametrici nella caratterizzazione del rischio stimato di malignità di noduli tiroidei sottoposti a citologia per ago sottile

Scopo Le società scientifiche hanno adottato sistemi per la classificazione ecografica dei noduli tiroidei, con l’obiettivo di ridurre gli agoaspirati senza perdere neoplasie clinicamente rilevanti. L’obiettivo del progetto è stato la validazione prospettica dell’accuratezza diagnostica di tali sistemi e la loro potenziale integrazione con i dati citologici tradizionali e di biologia molecolare. Metodi Sono stati prospetticamente valutati noduli sottoposti ad agoaspirato ecoguidato. Le caratteristiche ultrasonografiche sono state registrate ed utilizzate per classificare ciascun nodulo secondo le linee guida American Association of Clinical Endocrinologists (AACE/ACE/AME), American College of Radiologists (ACR), American Thyroid Association (ATA), EU-TIRADS e K-TIRADS. Lo standard di riferimento è l’istologia definitiva se disponibile, oppure una citologia benigna con successivo follow-up. Sono stati escluse citologie non diagnostiche o indeterminate. E’stato raccolto materiale residuo in soluzione conservante gli acidi nucleici, per studi di Next Generation Sequencing su pannello custom per carcinoma tiroideo. Risultati Sono stati campionati 917 noduli, di cui 82 sono stati esclusi per dimensioni <1 cm e 282 per assenza di diagnosi conclusiva. L’applicazione dei sistemi di classificazione permetterebbe di evitare da 92 (16.6%) a 287 (51.9%) agoaspirati (sistema K-TIRADS e ACR TIRADS, rispettivamente [p<0.001], con un false-negative rate di 3.3% e 2.8%). Il tasso di malignità nelle varie categorie risulta congruente con il rischio stimato. Conclusioni La stratificazione ecografica permette una migliore selezione dei noduli candidati a citologia ed eventuale analisi molecolare, attraverso la stima del rischio di malignità pre-test, ottimizzando i valori predittivi risultanti. I vari sistemi presentano differenze significative nel numero di prelievi evitabili

Client importance and audit quality in highly connected jurisdictions

The study focuses on the audit quality issue in three culturally and commercially highly connected jurisdictions with very different legal systems which affect auditors. Hong Kong practices common law, Taiwan practices civil law, and the People’s Republic of China (Mainland China) practices a socialist legal system. Taiwan adopts a civil law system with heavy influence by common law countries. It is therefore motivating to assess how auditors in each of the three connected jurisdictions with distinctive legal environments handle the audit quality for important clients. Accounting scandals and auditing frauds are perceived to be driven by aggressive companies and misrepresentation of audit reports. However, a locale’s legal system and law enforcements should affect the services auditors provide to their clients, particularly ‘important’ clients. I find that in all three jurisdictions, the more important the client to its auditor, the lower the audit quality as measured by restatement of financial statements. However, I find mixed results when using other measures of audit quality

Data-driven Inverse Optimization with Imperfect Information

In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent's objective function that best explains a historical sequence of signals and corresponding optimal actions. We focus here on situations where the observer has imperfect information, that is, where the agent's true objective function is not contained in the search space of candidate objectives, where the agent suffers from bounded rationality or implementation errors, or where the observed signal-response pairs are corrupted by measurement noise. We formalize this inverse optimization problem as a distributionally robust program minimizing the worst-case risk that the {\em predicted} decision ({\em i.e.}, the decision implied by a particular candidate objective) differs from the agent's {\em actual} response to a random signal. We show that our framework offers rigorous out-of-sample guarantees for different loss functions used to measure prediction errors and that the emerging inverse optimization problems can be exactly reformulated as (or safely approximated by) tractable convex programs when a new suboptimality loss function is used. We show through extensive numerical tests that the proposed distributionally robust approach to inverse optimization attains often better out-of-sample performance than the state-of-the-art approaches

Interobserver agreement of various thyroid imaging reporting and data systems

Ultrasonography is the best available tool for the initial work-up of thyroid nodules. Substantial interobserver variability has been documented in the recognition and reporting of some of the lesion characteristics. A number of classification systems have been developed to estimate the likelihood of malignancy: several of them have been endorsed by scientific societies, but their reproducibility has yet to be assessed. We evaluated the interobserver variability of the AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS classification systems and the interobserver concordance in the indication to FNA biopsy. Two raters independently evaluated 1055 ultrasound images of thyroid nodules identified in 265 patients at multiple time points, in two separate sets (501 and 554 images). After the first set of nodules, a joint reading was performed to reach a consensus in the feature definitions. The interobserver agreement (Krippendorff alpha) in the first set of nodules was 0.47, 0.49, 0.49, 0.61, and 0.53, for AACE/ACE/AME, ACR, ATA, EU-TIRADS, and K-TIRADS systems, respectively. The agreement for the indication to biopsy was substantial to near-perfect, being 0.73, 0.61, 0.75, 0.68, and 0.82, respectively (Cohen's kappa). For all systems, agreement on the nodules of the second set increased. Despite the wide variability in the description of single ultrasonographic features, the classification systems may improve the interobserver agreement, that further ameliorates after a specific training. When selecting nodules to be submitted to FNA biopsy, that is main purpose of these classifications, the interobserver agreement is substantial to almost perfect

Solving the single-track train scheduling problem via Deep Reinforcement Learning

Every day, railways experience small inconveniences, both on the network and the fleet side, affecting the stability of rail traffic. When a disruption occurs, delays propagate through the network, resulting in demand mismatching and, in the long run, demand loss. When a critical situation arises, human dispatchers distributed over the line have the duty to do their best to minimize the impact of the disruptions. Unfortunately, human operators have a limited depth of perception of how what happens in distant areas of the network may affect their control zone. In recent years, decision science has focused on developing methods to solve the problem automatically, to improve the capabilities of human operators. In this paper, machine learning-based methods are investigated when dealing with the train dispatching problem. In particular, two different Deep Q-Learning methods are proposed. Numerical results show the superiority of these techniques respect to the classical linear Q-Learning based on matrices.Comment: 12 pages, 4 figures (2 b&w